Isolation and characterization of Faecalibacterium prausnitzii from calves and piglets.

The goal of our study was to isolate and characterize Faecalibacterium prausnitzii from fecal samples of healthy calves and piglets, in order to develop a novel probiotic for livestock animals. We identified 203 isolates of Faecalibacterium sp., which were clustered in 40 genetically distinct groups. One representative isolate from each cluster was selected for further characterization. The concentrations of the short chain fatty acids (SCFA) acetate, butyrate, propionate and isobutyrate in the culture media were measured by gas chromatography. We observed reduction in the concentration of acetate followed by concomitant increase in the concentration of butyrate, suggesting that the isolates were consuming acetate present in the media and producing butyrate. Butyrate production correlated positively with bacterial growth. Since butyrate has many benefits to the colonic epithelial cells, the selection of strains that produce higher amounts of butyrate is extremely important for the development of this potential probiotic. The effect of pH and concentration of bile salts on bacterial growth was also evaluated in order to mimic the conditions encountered by F. prausnitzii in vivo. The optimal pH for growth ranged between 5.5 and 6.7, while most isolates were inhibited by of the lowest concentration of bile salts tested (0.1%). Antimicrobial resistance profile showed that most isolates of Faecalibacterium sp. were resistant against ciprofloxacin and sulfamethoxazole-trimethoprim. More than 50% of the isolates were resistant to tetracycline, amikacin, cefepime and cefoxitin. A total of 19 different combinations of multidrug resistance were observed among the isolates. Our results provide new insights into the cultural and physiological characteristics of Faecalibacterium prausnitzii illustrating large variability in short chain fatty acid production, in vitro growth, sensitivity to bile salts, and antibiotic resistance and suggesting that future probiotic candidates should be carefully studied before elected for in vivo studies

Equation of state of charged colloidal suspensions and its dependence on the thermodynamic route

The thermodynamic properties of highly charged colloidal suspensions in contact with a salt reservoir are investigated in the framework of the Renormalized Jellium Model (RJM). It is found that the equation of state is very sensitive to the particular thermodynamic route used to obtain it. Specifically, the osmotic pressure calculated within the RJM using the contact value theorem can be very different from the pressure calculated using the Kirkwood-Buff fluctuation relations. On the other hand, Monte Carlo (MC) simulations show that both the effective pair potentials and the correlation functions are accurately predicted by the RJM. It is suggested that the lack of self-consistency in the thermodynamics of the RJM is a result of neglected electrostatic correlations between the counterions and coions

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning

Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night

Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy to the training dataset which must resemble the images in the target task. The acquisition of a dataset involves annotating images, an arduous and expensive process, generally requiring time and manual effort. Thus, a challenging scenario arises when the target domain of application has no annotated dataset available, making tasks in such situation to lean on a training dataset of a different domain. Sharing this issue, object detection is a vital task for autonomous vehicles where the large amount of driving scenarios yields several domains of application requiring annotated data for the training process. In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented. For that, a model based on Generative Adversarial Networks (GANs) is explored to enable the generation of an artificial dataset with its respective annotations. The artificial dataset (fake dataset) is created translating images from day-time domain to night-time domain. The fake dataset, which comprises annotated images of only the target domain (night images), is then used to train the car detector model. Experimental results showed that the proposed method achieved significant and consistent improvements, including the increasing by more than 10% of the detection performance when compared to the training with only the available annotated data (i.e., day images).Comment: 8 pages, 8 figures, https://github.com/viniciusarruda/cross-domain-car-detection and accepted at IJCNN 201

A combined approach for comparative exoproteome analysis of Corynebacterium pseudotuberculosis

Background: Bacterial exported proteins represent key components of the host-pathogen interplay. Hence, we sought to implement a combined approach for characterizing the entire exoproteome of the pathogenic bacterium Corynebacterium pseudotuberculosis, the etiological agent of caseous lymphadenitis (CLA) in sheep and goats. Results: An optimized protocol of three-phase partitioning (TPP) was used to obtain the C. pseudotuberculosis exoproteins, and a newly introduced method of data-independent MS acquisition (LC-MSE) was employed for protein identification and label-free quantification. Additionally, the recently developed tool SurfG+ was used for in silico prediction of sub-cellular localization of the identified proteins. In total, 93 different extracellular proteins of C. pseudotuberculosis were identified with high confidence by this strategy; 44 proteins were commonly identified in two different strains, isolated from distinct hosts, then composing a core C. pseudotuberculosis exoproteome. Analysis with the SurfG+ tool showed that more than 75% (70/93) of the identified proteins could be predicted as containing signals for active exportation. Moreover, evidence could be found for probable non-classical export of most of the remaining proteins. Conclusions: Comparative analyses of the exoproteomes of two C. pseudotuberculosis strains, in addition to comparison with other experimentally determined corynebacterial exoproteomes, were helpful to gain novel insights into the contribution of the exported proteins in the virulence of this bacterium. The results presented here compose the most comprehensive coverage of the exoproteome of a corynebacterial species so far